Hyperspectral Image Unmixing via Alternating Projected Subgradients

We formulate the problem of hyperspectral image unmixing as
a nonconvex optimization problem, similar to nonnegative matrix
factorization.
We present a heuristic
for approximately solving this problem using an
alternating projected subgradient approach.
Finally, we present the result of applying this method on the
1990 AVIRIS image of Cuprite, Nevada and show that
our results are in agreement with similar studies on the same data.